The proliferation of traffic and surveillance cameras has led to an increased demand for object tracking in modern applications. Automated video analytics technologies have made real-world real-time surveillance possible. However, such real-world scenarios create a number of challenges traditional object tracking system are not equipped to handle. For example, occlusion, changes in scene illumination, weather conditions, object appearance characteristics, and camera shake cause known tracking methods and systems to fail.
While significant research efforts have been devoted to improving traffic and surveillance systems, they are typically limited to inefficient methods for tracking the direction and speed of objects in a traffic surveillance capacity. Therefore, there is a need for a robust and computationally efficient method and system that exploits regularized motion conditions to track objects in a scene.